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Showing 1 to 3 of 3 for “"stochastic bandit"”.
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Particle Thompson sampling
… is an effective Bayesian heuristic for solving stochastic bandit problems. But it is hard to implement in practice due to the intractability of maintaining a continuous posterior distribution. Particle Thompson sampling (PTS) is an approximation of Thompson sampling based on the simple idea of …
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Robust sequential decision-making on networks
… algorithms for two specific settings of the stochastic multi-armed bandit problem. The first setting considers the problem where rewards are drawn from a family of extremely heavy-tailed distributions known as a-stable distributions. For this setting, I extended an existing upper confidence …
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A NEW ZEROTH-ORDER ORACLE FOR DISTRIBUTED AND NON-STATIONARY LEARNING
… function has Lipschitz gradient. Then, for stochastic bandit optimization problems, we show that ZO with one-point residual feedback achieves the same convergence rate as that of two-point scheme with uncontrollable data samples.</p><p>Next, we apply the proposed one-point residual-feedback …